In the field of image processing for image enhancement, several methods for adjusting image contrast have been developed. Among the best known image enhancement techniques are the gamma correction and the histogram equalization technique.
The gamma correction technique is a simple exponential correction, and is commonly implemented in image processing. The histogram equalization technique has been proposed by different authors. Starting from the original grey level distribution of the image, the original image histogram is reshaped into a different one of uniform distribution to increase contrast.
Numerous improvements have been made to simplify equalization by introducing models of perception. Frei [4] introduced histogram hyperbolization that attempts to redistribute perceived brightness rather than grey levels. Frei approximated brightness using the logarithm of the luminance.
Other authors (for example, Mokrane [5]) have introduced methods that use more sophisticated models of perceived brightness and contrast. Wang et al. [6] proposed an equal area dualistic sub-image histogram equalization. First, the image is decomposed into two sub-images of equal area based on its original probability density function. Then, the two sub-images are singularly equalized. The processed sub-images are merged into one image. The authors have applied this technique in video systems.
In general, there are two classes of contrast correction techniques: global and local correction techniques. Let us consider a scene of a room illuminated by a window that looks out on a sunlit landscape. A human observer inside the room can easily see individual objects in the room, as well as features in the outdoor landscape. This is because our eyesight adapts itself locally as we scan the different regions of the scene.
If we attempt to photograph the same scene, the result could be disappointing. Either the window is overexposed and we can hardly see outdoor detail, or the interior of the room is under-exposed and looks dark.
Global contrast correction techniques produce the same disappointing results since it is difficult to accommodate both shadowed and highlighted detail. The advantage of the local contrast correction techniques is that they provide a method to map one input value onto many different output values, depending on the values of the neighboring pixels, thus allowing for simultaneous lowlight and highlight adjustments.
Different algorithms of local contrast correction have been proposed. The photographers' practice of “dodging and burning” to locally adjust print exposure in a darkroom, inspired an early paper by Chiu et al. [7] disclosing the construction of a locally varying attenuation factor M by repeatedly clipping and low-pass filtering the scene. Their method works well in smoothly shaded regions.
Larson et al. [8] presented a tone reproduction operator that preserves visibility in high dynamic range scenes. They introduced a new histogram adjustment technique, based on the population of local adaptation luminances in a scene.
Tumblin and Turk [9] devised a hierarchy that closely follows artistic methods for scene renderings. Each level of hierarchy is made from a simplified version of the original scene made of sharp boundaries and smooth shadings. They named the sharpening and smoothing method low curvature image simplifiers or LCIS. This technique proved itself effective in converting high contrast scenes to low contrast, highly detailed display images.
Battiato et al. [10] presented a collection of methods and algorithms able to deal with high dynamic ranges of images. All techniques rely on using differently exposed pictures of the same scene to enhance the usual 8-bit depth representation of pictures.
Pattanik et al. [11] developed a computational model of adaptation spatial vision for tone reproduction. Their model is based on a multiscale representation of pattern, luminance and color processing in the human visual system.
Jobson et al [12] and Rahman et al. [13] devised a contrast reduction method in accordance with Land's Retinex theory of lightness and color adaptation. They presented a multi-scale Retinex that simultaneously achieves enhanced color rendition and dynamic range performances.
Rizzi et al. [14] presented an algorithm for digital images unsupervised enhancement with simultaneous global and local effects, called ACE for Automatic Color Equalization. Inspired by some adaptation mechanisms of human vision, it realizes a local filtering effect by taking into account the color spatial distribution in the image. ACE proved to achieve an effective color constant correction and a satisfactory tone equalization by simultaneously performing global and local image corrections. However, the computational cost of the algorithm is very high.
Fairchild and Johnson [15] formulated a model called iCAM (image Color Appearance Model). The objective was to provide traditional color appearance capabilities, spatial vision attributes and color difference metrics in a simple model for practical applications, such as high dynamic range tone mapping, for example.
U.S. Pat. No. 6,741,753 to Moroney discloses a device and a method of color correction, wherein a mask is obtained from the image data. This mask is used for converting the image data in a color correction apparatus. The method involves the mask being derived from image data in a first pixel representation, and is used to convert the image data into a perceptual color space representation that is a function of a background luminance parameter. When converting the image data pixel-by-pixel into the perceptual color space representation, localized color correction is obtained by using varying mask values as the background luminance parameter for each pixel conversion.
Moroney [2] uses non-linear masking to perform local contrast correction. This correction can simultaneously lighten shadow areas and darken highlight areas and it is based on a simple pixelwise gamma correction of the input data. However, one of the limitations of Moroney's algorithm (common also to other local correction techniques) is the introduction of halo artifacts which are due to the smoothing across scene boundaries.